A GIS-Based System to Assess the Environmental Consequence of a Liquid Pipeline Rupture at Watercourse Crossings
Bibliographic record
Abstract
BGC Engineering Inc. (BGC) was retained by a large pipeline operator to develop a GIS-based system to assess and rank the environmental consequence of a pipeline rupture on watercourse crossings within their pipeline system. Several physical, biological and socio-economic factors contribute to the environmental consequence of a pipeline rupture on a watercourse. This study examined select spatial and vulnerability factors, and did not consider biologic or economic impacts. Three factors were selected as part of the initial study to prioritize the pipeline watercourse crossings according to: (1) size of the watercourse at the pipeline crossing, (2) proximity of each individual crossing to larger downstream watercourses, and (3) pipeline liquid flow rate volume. A spatial analysis was conducted to determine the first two factors, while input for the third factor was provided by the pipeline operator. Watercourse size was determined using Strahler’s stream order classification (Strahler 1952), while proximity to larger downstream watercourses was assessed using a Geographic Information System (GIS). This paper presents an overview of the data sources and methods used to develop an initial screening tool for identifying high consequence crossings within a pipeline system, and highlights the challenges encountered with acquiring and processing data to include in a consequence rating system. As with other pipeline risk assessments, the main challenges of this work include data availability, data integrity and resource limitations. This system is intended to fit within the pipeline operator’s current geohazard integrity management program and direct resources for a multi-year baseline field inspection program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".